High-performance computing (HPC) techniques are used in many industries and applications for implementing computationally intensive models or simulations. For example, the Department of Energy uses a large number of distributed compute nodes tightly coupled into a supercomputer to model physics experiments. In the oil and gas industry, parallel computing techniques are often used for computing geological models that help predict the location of natural resources.
High-performance computing applications typically require that simulation checkpoints and results are archived for long periods of time (such as several years). A small number of highly complex parallel file systems are typically employed to store the archived checkpoints and results. Such file systems are not economical in the sense that they need to solve challenging problems for a relatively small market.
An increasing number of companies and other enterprises are reducing their costs by migrating portions of their information technology infrastructure to cloud service providers. For example, virtual data centers and other types of systems comprising distributed virtual infrastructure are coming into widespread use.
Cloud object storage amortizes the software development and hardware infrastructure costs across a much larger number of parties, thereby reducing the cost significantly. In cloud-based information processing systems, enterprises in effect become tenants of the cloud service providers. However, by relinquishing control over their information technology resources, these cloud tenants expose themselves to additional potential security threats. For example, a given tenant may be inadvertently sharing physical hardware resources of a cloud computing environment with other tenants that could be competitors or attackers. Cloud storage systems have addressed such security concerns with multi-tenancy mechanisms.
A need exists for improved storage of archived checkpoints and results for high-performance computing applications.